[12] were grouped into five functional groups and plotted against CN. As expected, the R2 values then improved significantly (R2 and P values = 0.8717 and 2.10E ? 03 (haloalkane, n = 7), 0.8518 and 5.12E ? 05 (aromatic, n = 11), 0.9963 and 3.87E ? 02 (ketone, n = 3), 0.9243 and 5.50E ? 04 (aromatic ketone, n = 7), and 0.8929 and 2.12E ? 01 (phthalate, n = 3)). As such, these obtained results are Enzastaurin Phase 3 far better than the original results simply examined against MW (R2 = 0.4427 and P value = 1.13E ? 06) (Figure 2). The results of our comparative efforts to reevaluate the two previous studies [4, 12] consistently confirm that the use of the modified statistical approach tested in this study can be used to produce improved predictions of CLASS in relation to selected reference chemical group.

Figure 2 Reinterpretation of the mass sensitivity data by Allgood et al. [12] against carbon number.4. ConclusionsThe external calibration has been the most commonly used method for the quantitative analysis of diverse VOCs in environmental media. If the number of detected VOCs exceeds hundreds to thousands, assessment of all individual components in a quantitative sense is not easy. This is because all detected VOCs cannot be standardized (because of unavailability of a standard material, etc.). In this study, 18 VOCs representing the six functional groups were selected as the reference to develop predictive equations to assess the concentrations of CLASS belonging to any of those functional groups.

To find the optimal predictive equations of each reference compound, we conducted a series of LR analyses between their actual RF values (derived by external calibration of the liquid standard by the sorbent tube method) and three physicochemical properties (of the model compounds): (1) CN, (2) MW, and (3) BP.As a means to validate the applicability of the predicting equations for CLASS, a total of 18 reference VOCs were arbitrarily classified into 29 VOC groups by the combination of the raw six functional groups. Then, the reliability of this approach was evaluated by assigning the six best fit equations to each of all six groups and examined in terms of the PD value between different RFs. If the optimal PD values of each reference compound are derived for each of all 18 compounds, they averaged as low as 5.60 �� 5.63% (range of 0.27% (Ester) to 18.6% (PA)).

As a result, we were able to demonstrate the possibility that the projected RF values of the 18 reference VOCs, if assessed Brefeldin_A by this statistical approach, can comply well with their actual RF values determined experimentally. In other words, if the predictive equations were used to estimate the concentration of CLASS in real environmental samples, it is possible to derive quantitative concentration data for the CLASS with a fairly low experimental uncertainty.